Cellular-enabled Collaborative Robots Planning and Operations for Search-and-Rescue Scenarios
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Abstract (arXiv)
Mission-critical operations, particularly in the context of Search-and-Rescue (SAR) and emergency response situations, demand optimal performance and efficiency from every component involved to maximize the success probability of such operations. In these settings, cellular-enabled collaborative robotic systems have emerged as invaluable assets, assisting first responders in several tasks, ranging from victim localization to hazardous area exploration. However, a critical limitation in the deployment of cellular-enabled collaborative robots in SAR missions is their energy budget, primarily supplied by batteries, which directly impacts their task execution and mobility. This paper tackles this problem, and proposes a search-and-rescue framework for cellular-enabled collaborative robots use cases that, taking as input the area size to be explored, the robots fleet size, their energy profile, exploration rate required and target response time, finds the minimum number of robots able to meet the SAR mission goals and the path they should follow to explore the area. Our results, i) show that first responders can rely on a SAR cellular-enabled robotics framework when planning mission-critical operations to take informed decisions with limited resources, and, ii) illustrate the number of robots versus explored area and response time trade-off depending on the type of robot: wheeled vs quadruped.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Mar 2024.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 4.
5 papers.
- 2024SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systemscited by 1
- 2026MapViT: A Two-Stage ViT-Based Framework for Real-Time Radio Quality Map Prediction in Dynamic Environmentscited by 0
- 2026Interpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio Fusioncited by 0
- 2026Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraintscited by 0
- 2026D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systemscited by 0
- 2026D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems7 Sep 2026
- 2026Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints5 Jun 2026
- 2026Interpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio Fusion27 Feb 2026
- 2026MapViT: A Two-Stage ViT-Based Framework for Real-Time Radio Quality Map Prediction in Dynamic Environments22 Jan 2026
- 2024SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems4 Nov 2024